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Thoughtworks Technology Podcast

How fitness functions can help us govern and measure AI

Mar 6, 2025
Rebecca Parsons, former CTO emerita of ThoughtWorks and co-author of 'Building Evolutionary Architectures', joins Neal Ford, a regular host and also co-author, to dive into the dynamic world of AI governance. They explore how fitness functions can optimize AI performance, ensuring systems meet their intended goals. The duo discusses identifying biases within AI, the importance of operationalizing large language models, and the need for objective metrics in rapidly changing tech landscapes. Their insights reveal how adaptability can shape the future of AI.
42:01

Podcast summary created with Snipd AI

Quick takeaways

  • Fitness functions provide objective metrics that help organizations measure AI system goals, ensuring they meet defined performance criteria.
  • Collaborative ownership of fitness functions between architects and developers fosters shared responsibility, enhancing the effectiveness and relevance of AI systems.

Deep dives

Understanding Fitness Functions

Fitness functions are key metrics derived from evolutionary computation principles used to optimize complex systems. They provide a clear definition of desirable outcomes, allowing for the evaluation of various solutions to specific problems, such as minimizing distances in the traveling salesman problem. An important characteristic of these functions is their objectivity, which ensures that everyone agrees on whether a particular outcome meets the defined criteria. The ability to quantify vague concepts like maintainability into measurable fitness functions enables teams to automate checks, ultimately freeing them to focus on more nuanced architectural discussions.

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